Industrial Semi-Supervised Dynamic Soft-Sensor Modeling Approach Based on Deep Relevant Representation Learning

Abstract

Soft sensors based on deep learning have been growing in industrial process applications, inferring hard-to-measure but crucial quality-related variables. However, applications may present strong non-linearity, dynamicity, and a lack of labeled data. To deal with the above-cited problems, the extraction of relevant features is becoming a field of interest in soft-sensing. A novel deep representative learning soft-sensor modeling approach is proposed based on stacked autoencoder (SAE), mutual information (MI), and long-short term memory (LSTM). SAE is trained layer by layer with MI evaluation performed between extracted features and targeted output to evaluate the relevance of learned representation in each layer. This approach highlights relevant information and eliminates irrelevant information from the current layer. Thus, deep output-related representative features are retrieved. In the supervised fine-tuning stage, an LSTM is coupled to the tail of the SAE to address system inherent dynamic behavior. Also, a k-fold cross-validation ensemble strategy is applied to enhance the soft-sensor reliability. Two real-world industrial non-linear processes are employed to evaluate the proposed method performance. The obtained results show improved prediction performance in comparison to other traditional and state-of-art methods. Compared to the other methods, the proposed model can generate more than 38.6% and 39.4% improvement of RMSE for the two analyzed industrial cases.

Manufacturing industry based on dynamic soft sensors in integrated with feature representation and classification using fuzzy logic and deep learning architecture

“…As a result, various industrial applications of soft sensors based on SAE [ 14 ] are presented. Same authors improved this result significantly by utilizing a TDNN in [ 15 ].Mean error dropped to just 1.14 to 1.32% and 1.65° to 3.08°, in the same conditions utilized in [ 16 , 17 ]. As a result, the type of network used in these two papers had a significant impact on the algorithms’ performance.…”

“…Authors of [ 20 ] presents a novel convolutional, BiGRU, and Capsule network-based deep learning model, HCovBi-Caps, to classify the hate speech and authors of [ 21 ] introduce BiCHAT: a novel BiLSTM with deep CNN and hierarchical attention-based deep learning model for tweet representation learning toward hate speech detection. Authors of [ 15 ] do not use raw rotational speed signal, but instead translate it into frequency domain as well as process only first 20 harmonics, to earlier research are used an RBF network. They also employ structure-borne sound signal’s 21st–50th harmonics.…”

A data‐driven soft sensor based on weighted probabilistic slow feature analysis for nonlinear dynamic chemical processes

“…In practice, it is very difficult to obtain the key quality indexes in time due to the bad measuring environment, expensive measuring instruments, and measurement lag. Soft sensor technology uses the internal information of process data to build a regression model between key variables and auxiliary variables 5 . With the application of distributed control system (DCS), a large amount of process data reflecting the real process conditions can be collected and stored.…”

“…Soft sensor technology uses the internal information of process data to build a regression model between key variables and auxiliary variables. 5 With the application of distributed control system (DCS), a large amount of process data reflecting the real process conditions can be collected and stored. Thus, data-driven soft sensors have drawn increasing popularity and attentions in industrial fields.…”

Developing accurate data-driven soft-sensors through integrating dynamic kernel slow feature analysis with neural networks

“…RNNs suffers from gradient vanishing and explosions problems, however, the long shortterm memory (LSTM) network was developed to overcome this problem (Hochreiter & Schmidhuber, 1997). Several approaches have been developed to improve RNNs and LSTM networks for soft-sensor modelling (Curreri et al, 2021;Moreira de Lima & Ugulino de Araújo, 2021). Yuan et al (2020) propsoed a supervised LSTM network to focus on learning the quality-relevant dynamics as opposed to just the dynamics of the input variables.…”